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A visualized method to precisely generate sensor positions based on required geo-fence area

Publishing Venue

The IP.com Prior Art Database

Abstract

Geo-fence is a virtual perimeter for a real-world geographic area. When the location-aware device of a location-based service (LBS) user enters or exits a geo-fence, the device receives a generated notification. With the exponential growth of mobile device users, this technology is used in many different industries. For example in the Retail industry, the merchants could have marketing promotions in different area of the store through different geo-fence. It could distribute location specific ads to customers in target area on their mobile devices. Now the common way to deploy the geo-fence is based on the experience. It needs several rounds of adjustments to have the area match the required area. In this visualized method, we introduce an interactive method to generate sensor positions based on the geo-fence area, which is created by the users based on the required geo-fence area.

Country

Undisclosed

Language

English (United States)

This text was extracted from a PDF file.

This is the abbreviated version, containing approximately
48% of the total text.

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A visualized method to precisely generate sensor positions based on required geo

A visualized method to precisely generate sensor positions based on required geo-

This visualized method has the advantages below:

Advantages:

a. Users could define the geo-fence area very easily through visualized way, and add obstacles points if some area is not appropriate to

deploy sensors.

b. It could generate the sensors for the required geo-fence area precisely. Based on the map as well as what area users want to define the

geo-fence, and the coverage rate of the geo-fence in this area, the system will generate the location and numbers for the needed sensors .c. It could save the cost for adjustments and better utilize the usage of the sensors .

d. People without technical skills could generate the geo-fence on demand very easily.

We will explain the visualized method to precisely generate geo-fence area in retail industry. The scenario is, user want to define geo-fence for marketing promotions. Below in Figure 1 is a map for the store.

--fence area

fence area

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Figure 1. Map of the Store

User wants to ensure this marketing promotion is only happened when users enters the area of Woman 's Apparel, Boys Apparel and Diet and

Nutrition.

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He could define the shape and the location of the geo -fence area through visualized way as showed in Figure 2.

Figure 2. Geo-fence on the map

This could be achieved easily in the system. Simple free transform functions could help to define this geo -fence area. We skip this part since

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it's not the focus we claim in this method.

Implementation

The processing flow is shown as the Figure 3.

Figure 3. System processing flow diagram

1. Getting sensing radius and expected coverage rate

The user first input the sensor reliable sensing radius to the system . This can be preset in the system by different type of current popular sensors and selected by user, or directly input by users with value.

The coverage rate is a value between 0 to 1, which will determine the density of the sensors on the map. The sensor density will increase with this coverage rate.

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2. Drawing obstacles on the map

Users should draw up the obstacles which would lead to the difficulty on deploying sensors on the map , so that the calculation would be more accurate.

3. Regularizing the edge of the area

The selected area is regularized to the closest regular shape with straight lines . The processed area edge would be like Figure 4.

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Figure 4. Processed area edge

4.Calculating the sensor positions

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Calculate the sensor positions with the sensing radius and expected coverage rate , according to the algorithm below, as shown in Figure 5:

Figure 5. Sensor position calculating flow diagram

4.1 Deploying sensors on the edge of the area and obstacles

Firstly, we donate the sensing radius of the current sensor as rs. Then as shown in Figure 6, we deploy sensors in diff...